Fire Detection and Verification using Convolutional Neural Networks, Masked Autoencoder and Transfer Learning
Publish place: majlesi Journal of Electrical Engineering، Vol: 16، Issue: 4
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_MJEE-16-4_002
تاریخ نمایه سازی: 25 بهمن 1401
Abstract:
Wildfire detection is a time-critical application since it can be challenging to identify the source of ignition in a short amount of time, which frequently causes the intensity of fire incidents to increase. The development of precise early-warning applications has sparked significant interest in expert systems research due to this issue, and recent advances in deep learning for challenging visual interpretation tasks have created new study avenues. In recent years, the power of deep learning-based models sparked the researcher’s interests from a variety of fields. Specially, Convolutional Neural Networks (CNN) have become the most suited approach for computer vision tasks. As a result, in this paper we propose a CNN-based pipeline for classifying and verifying fire-related images. Our approach consists of two models, first of which classifies the input data and then the second model verifies the decision made by the first one by learning more robust representations obtained from a large masked auto encoder-based model. The verification step boosts the performance of the classifier with respect to false positives and false negatives. Based on extensive experiments, our approach proves to improve previous state-of-the-art algorithms by ۳ to ۴% in terms of accuracy.
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Authors
Zainab Abed Almoussawi
College of Islamic Science, Ahl Al Bayt University
Raed Khalid
Medical technical college; Al-farahidi University
Zahraa Salam Obaid
Building and Construction Engineering Technology Department, AL-Mustaqbal University College
Zuhair I. Al Mashhadani
Al-Nisour University College, Baghdad, Iraq
Kadhum Al-Majdi
Department of biomedical engineering, Ashur University College, Baghdad, Iraq
Refad E. Alsaddon
Department of Prosthetic Dental Technology, Hilla University college, Babylon, Iraq
Hassan Mohammed Abed
Mazaya University College, Iraq
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